QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks-Reference-Cited by-同舟云学术

QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
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Author:

Zhang Yedi¹,Zhao Zhe²,Chen Guangke²,Song Fu²,Zhang Min³,Chen Taolue⁴,Sun Jun⁵

Affiliation:

1. ShanghaiTech University, China and Singapore Management University, Singapore

2. ShanghaiTech University, China

3. East China Normal University, China

4. Birkbeck, University of London, United Kingdom

5. Singapore Management University, Singapore

Funder

An oversea grant from the State Key Laboratory of Novel Software Technology, Nanjing University

Ministry of Education, Singapore under its Academic Research Fund Tier 3

National Key Research Program

Birkbeck BEI School Project

National Natural Science Foundation of China (NSFC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3551349.3556916

Reference89 articles.

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2. Optimization and abstraction: a synergistic approach for analyzing neural network robustness

3. Apollo. 2018. An open reliable and secure software platform for autonomous driving systems. http://apollo.auto. Apollo. 2018. An open reliable and secure software platform for autonomous driving systems. http://apollo.auto.

4. DeepAbstract: Neural Network Abstraction for Accelerating Verification

5. Teodora Baluta , Zheng Leong Chua , Kuldeep S Meel , and Prateek Saxena . 2021 . Scalable quantitative verification for deep neural networks . In 2021 IEEE/ACM 43rd International Conference on Software Engineering. IEEE, 312–323 . Teodora Baluta, Zheng Leong Chua, Kuldeep S Meel, and Prateek Saxena. 2021. Scalable quantitative verification for deep neural networks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering. IEEE, 312–323.

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